The rapid development of Brain Computer Interface technology has led to its application in the fields of health monitoring and rehabilitation. However, the precise identification of EEG signals continues to be a pivotal challenge. In order to solve the problem of low identification rate of visually stimulated EEG signals, this paper designs multimodal visual stimulation EEG signal identification method based on Convolutional Spiking Neural Network. The four directions of the images are employed to stimulate the brain to produce EEG signals. The EEG signal identification process is as follows. Firstly, the dataset is constructed by obtaining and pre-processing EEG signals for multimodal visual stimulation with MI and SSVEP. Secondly the C-SNN structure is designed and the network model parameters are optimized. The experimental results show that the C-SNN network model designed in this paper can effectively identify the fused MI and SSVEP EEG signals, with an identification accuracy of 95%. The advancement of Brain Inspired intelligent technology has facilitated the development of Brain Computer Interaction technology.
KEYWORDS: Biogases, Computing systems, Principal component analysis, Control systems, Signal processing, Data communications, Statistical analysis, Sensors, Liquids, Engineering
In order to better develop the industrial biogas treatment engineering, it is necessary to conduct real-time data collection and monitoring of the anaerobic fermentation process, and then make statistics, analysis and scientific decision-making. In this paper, 5g[1] and remote radio (lora) technology are introduced to monitor the anaerobic dry fermentation system, and various architectures of the online monitoring and control system for anaerobic fermentation are designed. Finally, the monitoring and diagnosis experiment was designed, remotely collecting the data of anaerobic dry fermentation process as the observation object and conducting statistical analysis. The results show that 5g and lora technology have strong applicability in the anaerobic fermentation process, which can effectively improve the level of online monitoring technology of biogas treatment.
KEYWORDS: Brain, Neurons, Neural networks, Control systems, Artificial neural networks, Artificial intelligence, Data processing, Visualization, Signal processing, Nervous system
Brain-inspired intelligence is recognized as one of the important research directions of artificial intelligence. It is also an important supplement to brain science and brain medicine. At present, the research of Brain-inspired intelligence still focuses on traditional computer science. There are still many difficulties to overcome on the way of brain inspiration and imitation. In this paper, based on the Spiking Neural Network, the traditional Spiking Neural Network is optimized by brain inspired method to establish a Brain-inspired cooperative control system. This system will provide a new idea for the development of humanoid robot and exoskeleton technology.
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